“DIGITAL MARKETING STRATEGIES” Leveraging The “Back-End” Tools Professional Background RACING INDUSTRY EXPERIENCE:

• First Job Out of Undergrad: - Arlington Park, Assistant to the VP of Marketing - Sponsorship Manager • Director of Marketing – Los Alamitos Racecourse

CLASICALLY TRAINED CONSUMER BRAND MARKETER: • Industry Experience: Retail, Agency, Manufacturing, CPG and Consulting. Developed & launched +1,500 products across 45 different categories

EDUCATION: B.S. in Agribusiness – Cal Poly University - San Luis Obispo, CA M.B.A. – Pepperdine University - Malibu, CA

THOROUGHBRED OWNER: Red Apache, Grandson of Tabasco Cat 2 We Call Him Riggs! Purchased “Off-the-Track” (Turf Paradise) in 2014 as a 4 YO.

3 Agenda • What is “Big Data”? - Creating a Frame of Reference - The “4 V’s” – Drivers of Big Data

• Data Sources & Technology Landscape

• Data Types: Structured vs. Unstructured

• Consumer Insights & Today’s Systems - Implementing Unstructured Data - Data Visualization

Social Media Influencers - The “Klout” Score - Identifying Social Media Influencers - Measuring Influencer Value

• Identifying Racing Data

4 • Key Takeaways Big Data is like teenage sex:

- Everyone talks about it - Nobody really knows how to do it - Everyone thinks everyone else is doing it - So everyone claims they are doing it

5 What is Big Data? Data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a reasonable amount of time.

6 Data Frame of Reference Megabyte (MB) - A good sized novel.

Gigabyte (GB) - 1600 books. About 300 MP3s.

Terrabyte (TB) – 1.6M books. 30 weeks worth of high-quality audio.

Petabyte (PB) – 160M books.

Exabyte (EB) – 3000 times the entire content of the Library of Congress.

Zettabyte (ZB) – 1 billion Terrabytes; Two hundred billion DVDs.

Yottabyte (YB) – 1 trillion Terrabytes. 7 The “Four V’s” – Drivers of Big Data

8 Data Sources

9 Social Media Landscape

10 Marketing Technology Landscape

11 Structured vs. Unstructured - Defined Data is classified as either Structured or Unstructured.

• Structured Data refers to information that resides in a traditional row-column database—like Excel.

• Unstructured Data refers to information that doesn't reside in a traditional row-column database.

NOTE: Experts estimate that 80 to 90 percent of the data in any organization is unstructured.

12 Examples of Structured Data Structured Data usually refers to information that resides in a traditional row-column database—like Excel. Here the data is stored in fields in a database

13 Examples of Unstructured Data Unstructured Data files often include text and multimedia content. Examples include email messages, word docs, videos, photos, audio files, presentations, etc. This data doesn't fit neatly in a database.

14 Consumer Insights Today’s systems can structure the unstructured, then correlate key internal data with the relevant social media universe – revealing new, actionable insights.

15 Implementing Unstructured Data Big Data Tools Software like Hadoop or Oracle Endeca can process both unstructured and structured data that are extremely large, very complex and changing rapidly.

Data Integration Tools Combine data from disparate sources to be analyzed from a single application & the capability to unify structured and unstructured data.

Search and Indexing Tools These tools retrieve information from unstructured data files such as documents, Web pages and photos 16 Data Visualization Data visualization is the presentation of data in a pictorial or graphical format.

17 Social Media Influencers A Social Influencer is one who: • Has the maximum followers • Can influence others easily • Creates and shares content regularly

Benefits of identifying Social Influencer: • Leverage 3rd-Party credibility (others) • Expand the message & the business

How to Identify the Influencers: • Scores like “Klout” score are available to measure the influence of someone in social media 18 The Klout Score

• Klout is a digital service that uses to rank its users according to online social influence via the "Klout Score“

• Klout measures influence by using data points from various sites – , , Google+, LinkedIn, etc., and Klout itself. – Count, follower count, retweets, list memberships, influential follower retweets unique mentions. Information is blended with data from other social network followings & interactions to come up with the Klout Score.

19 Identifying Social Media Influencers

When developing an influencer outreach campaign, make sure you’ve got a good “READ” on the situation!

20 Measuring Influencer Value Several Key Performance Indicators (KPIs) can be combined into meaningful ratios to help measure audience activity and engagement.

• Sentiment • Re-tweets • Forward to a friend • Social media sharing • Comments • Like or rate something • Reviews • Contributors and active contributors • Page views • Unique visitors • Traffic from social networking sites • Time spent on site • Response time 21 Identifying Racing Data • Wagering volumes, on-track/off-track/on-line • Loyalty program information • Attendance • Non-wagering revenues (F&B, Parking, Merchandise) • Social Media sites, racing blogs • Odds • Race quality/types • Results • Handicapping data • Performance history • Wager types: win, place, show, exotics • Weather • Seasonality • Track condition • Special events 22 Key Takeaways • Marketing Analytics is the primary opportunity-driver of business growth going forward. • “Dip your toe in the water now!”. • Unstructured data will reveal new value and actionable insights. • Operators need to take the long-view and invest to grow the sport.

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